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Advancing the Understanding of Immigration, Crime, and Crime Reporting at the Local Level with a Synthetic Population

Award Information

Award #
2020-R2-CX-0027
Congressional District
Status
Awarded, but not yet accepted
Funding First Awarded
2020
Total funding (to date)
$899,954

Description of original award (Fiscal Year 2020, $899,954)

This research has implications for the understanding of trends in immigration and different immigrant destinations and trends in crime and crime reporting. Given the methodological rigor, the study will be able to both broadly and specifically describe the relationship between illegal immigration and crime in the United States by type of crime. Furthermore, it will offer unique insight into crime reporting for communities with illegal immigrants. This will aid in contextualizing findings and determining whether a “crime reduction” effect of immigration exists, or whether the effect results from systematic underreporting.
The study proposes to analyze the relationship between legal and illegal immigration, crime, and the reporting of crime. It seeks to advance the understanding of the nature of immigration and crime by:
1. Improving on previous methods of estimating the illegal immigrant population by incorporating national and local information to predict documentation status.
2. Examining the relationship of different types of crime and crime reporting by the percentage of illegal and legal residents at the census tract level.
3. Exploring whether the change in the proportion of immigrants in an area, both over time and space, is associated with change in crime rates and crime reporting.
4. Contrasting these relationships in traditional and emerging immigrant destinations and in places with low levels of immigration.
The study design proposes to:
1. Collect and compile detailed incident-based crime reports (IBR) and calls for service (CFS) data to create census tract-level crime-specific offending rates and crime reporting rates for all census tracts in a selection of police jurisdictions.
2. Use cutting-edge statistical techniques in simulation and machine learning, in combination with the U.S. Census Bureau Survey of Income and Program Participation (SIPP), to create a geographically accurate model to predict documentation status and apply it to a unique synthetic population of selected jurisdictions at the census tract level.
3. Conduct spatial and temporal analyses of the relationship between different types of crime and reporting of crime, nativity status, and legal residency for the foreign born.
Note: This project contains a research and/or development component, as defined in applicable law, and complies with Part 200 Uniform Requirements - 2 CFR 200.210(a)(14). CA/NCF

Date Created: September 16, 2020